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Can I read A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography on EtoBox?
A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography by Margarida Antunes; Duarte Folgado; Marília Barandas; André Carreiro; Carla Quintão; Mamede de Carvalho; Hugo Gamboa is a Medicine article available to read on EtoBox.
What is A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography about?
Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. Nowadays, needle electromyography (nEMG) is the standard practice for electrodiagnosis of ALS. Surface electromyography (sEMG) is emerging as a more practical and less painful alternative to nEMG but still has analytical and technical challenges. The objective of this work was to study the feasibility of using a set of morphological features extracted from sEMG to support a machine learning pipeline for ALS diagnosis. We developed a novel feature set to characterize sEMG based on quantitative measurements to surface representation of Motor Unit Action Potentials. We conducted several experiments to study the relevance of the proposed feature set either individually or combined with conventional feature sets from temporal, statistical, spectral, and fractal domains. We validated the proposed machine learning pipeline on a dataset with sEMG upper limb muscle data from 17 ALS patients and 24 control subjects. The results support the utility of the proposed feature set, achieving an F 1 score of (81.9 ± 5.7) for the onset classification approach and (83.6 ± 6.9) for the subject classification approach, sole
Who reads A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Margarida Antunes; Duarte Folgado; Marília Barandas; André Carreiro; Carla Quintão; Mamede de Carvalho; Hugo Gamboa
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Medicine (Physical Sciences)